Hybrid genetic algorithm for solving Traveling Salesman Problem with sorted population

The performance of Genetic Algorithms (GA) is affected by various factors such as parameters, genetic operators and strategies. The traditional approach with random initial population is efficient however the whole initial population may contain infeasible solutions, which causes GA take longer to converge. The GA was modified in various ways to achieve faster and better convergence and it was particularly recognized by the researchers that initial population greatly affects the performance of GA. This study proposes modified GA with sorted initial population and applies it to solving Travelling Salesman Problem (TSP). Normally the bigger the initial the population the more computationally expensive the calculation becomes with each generation. New approach will allow reducing the size of the initial problem while preserving a better fit population and thus leading to faster and better convergence. G,4 is based on theory of natural selection and new approach sorts out initial population so that only those with higher fitness are retained. It gives grounds to assume that a better solution can be obtained and in a shorter time. Moreover, new approach proposes that genetic algorithm has less probability of being stuck in the local optimum and rather produces a result close to global optimum. The proposed approach is tested on a simulator built using object-oriented approach and the test results prove the validity of the proposed method.